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Discriminant Analysis for Human Arm Motion Prediction and Classifying

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DOI: 10.4236/ica.2013.41004    4,723 Downloads   6,446 Views   Citations

ABSTRACT

The EMG signal which is generated by the muscles activity diffuses to the skin surface of human body. This paper presents a pattern recognition system based on Linear Discriminant Analysis (LDA) algorithm for the classification of upper arm motions; where this algorithm was mainly used in face recognition and voice recognition. Also a comparison between the Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) algorithm is made for the classification of upper arm motions. The obtained results demonstrate superior performance of LDA to k-NN. The classification results give very accurate classification with very small classification errors. This paper is organized as follows: Muscle Anatomy, Data Classification Methods, Theory of Linear Discriminant Analysis, k-Nearest Neighbor (kNN) Algorithm, Modeling of EMG Pattern Recognition, EMG Data Generator, Electromyography Feature Extraction, Implemented System Results and Discussions, and finally, Conclusions. The proposed structure is simulated using MATLAB.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Al-Faiz and S. Ahmed, "Discriminant Analysis for Human Arm Motion Prediction and Classifying," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 26-31. doi: 10.4236/ica.2013.41004.

References

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